Surface‐enhanced Raman scattering spectroscopy for potential noninvasive nasopharyngeal cancer detection
Bibliographic record
Abstract
Combining membrane electrophoresis with surface‐enhanced Raman scattering (SERS) spectroscopy, the serum proteins were first purified and then mixed with silver nanoparticles to perform SERS spectral analysis. Therefore, the spectral signatures were enhanced to high‐fidelity SERS signatures because of the purification procedure of the first step. We used the method to analyze blood plasma samples from nasopharyngeal cancer patients (n = 43) and healthy volunteers (n = 33) for cancer detection. Principle component analysis of the SERS spectra revealed that the data points for the cancer group and the normal group form distinct, completely separated clusters with no overlap. Therefore, the nasopharyngeal cancer group can be unambiguously discriminated from the normal group, i.e., with both diagnostic sensitivity and specificity of 100%. These results are very promising for developing a label‐free, noninvasive, and reliable clinical tool for rapid cancer detection and screening. Copyright © 2011 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".